Managing Risk | Part 3: Using a Dynamic Approach to Asset Allocation

Author: Nathan Rowader
Date: September 19, 2014
Category: Financial Planning
Tags: , , , , ,

We began our managing risk series two weeks ago with a discussion of the complicated nature of averages and why using them can be damaging when constructing a portfolio. Last week, we delved into a simple method for forecasting returns for major asset classes. This week, we are going to continue our look into the core inputs of portfolio construction by focusing on volatility.

As you might recall, building a well-constructed portfolio relies on three inputs:

  • Return
  • Volatility
  • Correlation

I believe that volatility is one of the most important inputs in portfolio construction since it is really the only part of the portfolio you can control through the selection of assets (i.e., stocks vs. bonds). Calculating volatility is also a great method for determining how a portfolio’s allocations should be weighted in order to maximize potential return. However, it is probably the most difficult input to understand. So, let’s start off with a definition of volatility and then we will look at how volatility forecasting can help out a portfolio.

Understanding volatility
For investors, volatility is a measure in the variation of an investment’s price over time. In other words, an investment with an average daily price swing of 1% to 2% will be more volatile than an investment with an average daily price swing of 0.25% to 0.50%. However, volatility is not a measure of directionality; it doesn’t necessarily tell you if an investment is going up or down for the long term.

How is volatility measured and what does it mean?
There are actually dozens if not hundreds of ways to measure volatility, but standard deviation is likely the most common method. Standard deviation is a measurement of variation from an average over a set period of time. To illustrate this idea, the S&P 500 Index has a realized annual standard deviation of 13.9% over the three years ending December 31, 2013, and an average annual return of 16.07% during the same time period. We can interpret this data to mean that the S&P 500 will fall within +/- 13.9% of the average return of 16.07%. This tells us that the S&P 500 is not likely to lose any money on average, which is good. The wrinkle is that this only occurs in roughly 60% of the distribution, which may not be accurate enough for our portfolio construction process. We can increase confidence by calculating the possible return at +/- 27.8% (2 x 13.9% or two standard deviations from the average) of 16.07%, which covers roughly 95% of the distribution. This is of course very different than our first estimate. Hopefully you can see why getting this calculation to a reasonable number is very important since it is critical to understanding the success rate of achieving your financial goals.

Despite volatility being a critical input, many investors don’t pay much attention to it. When an investor creates an asset allocation portfolio, the process usually includes some type of risk assessment based on how that investor balances risk and reward. These assumptions of gain and loss are usually based on the historical level of volatility of an asset allocation portfolio. For example, after completing a risk questionnaire, an investor might believe that he or she is a 50% stock investor and a 50% bond investor, which has a historical standard deviation of roughly 10%. However, in practice, a portfolio of 50% S&P 500 and 50% Barclays U.S. Government/Credit Bond Index is often quite a bit less than 10% (not taking enough risk) and occasionally well above 10% (taking too much risk). To illustrate, see the table below. As a result, an investor is likely to leave a lot of return on the table by keeping their allocation static regardless of the level of volatility. So, let’s take a look at a way that an investor can avoid this problem.

2014-09-blog-nrowader-volatility

Volatility-based rebalancing
There are many ways to handle changing volatility levels in the market, but the method doesn’t have to be complicated. It is important that it be dynamic so that your view on the market is as current as possible. To illustrate one such method, I took a rolling six-month average of realized standard deviations of the S&P 500 and the Barclays U.S. Aggregate Bond Index and then rebalanced the portfolio on a monthly basis to a target of 10% (similar to a 50% stock, 50% bond portfolio). Below are the results of this hypothetical exercise over the past 10 years compared to a 50% stock, 50% bond mix.

2014-09-blog-nrowader-growth-of-100

As you can see, this simple method of rebalancing to a target level of volatility produced higher returns to the tune of about 1.6% more per year. The strategy did increase volatility, but in this case that is a good thing since during this particular market more volatile stocks were a good place to be. Additionally, the mix does employ shorting and leverage. Hitting the target level of 10% occasionally includes shorting bonds and buying stock or vice versa, but the total mix is always kept to 100%. For example, in February of 2007 the mix would have required 137% in the S&P 500 but in February 2009 the mix would require a short position of 15% in the S&P 500. This is a very different approach to asset allocation, but as demonstrated, it can deliver different results.

You might also notice that the hypothetical mix above didn’t actually deliver on the 10% target level of volatility. This is mostly due to the limitation that the portfolio balances out to 100%. In other words, no actual borrowing was used to achieve leverage. Additionally, our use of a moving average isn’t necessarily the best way to target volatility. I chose it because it was easy to understand, but more advanced methods would get us closer to the target and potentially a better return.

Finally, you will notice that I didn’t discuss the use of correlations in the example above. Correlations are tricky because they can dramatically shift allocation. So, I stuck with the realized correlation between stocks and bonds over the 10 years prior to the example (0.1% to be precise). Similar to volatility, estimating correlation is a fruitful endeavor, but is more complicated. My recommendation would be to use something longer term, like rolling 52-week correlations, as an input to a volatility rebalanced model.

In my next and final post for the managing risk series, we will explore putting together volatility and expected returns.

See the rest of the series: Part 1 | Part 2 | Part 4

RISKS

Investing involves risk, including possible loss of principal. The value of any financial instruments or markets mentioned herein can fall as well as rise. Past performance does not guarantee future results.

This material is distributed for informational purposes only and should not be considered as investment advice, a recommendation of any particular security, strategy or investment product, or as an offer or solicitation with respect to the purchase or sale of any investment. Statistics, prices, estimates, forward-looking statements, and other information contained herein have been obtained from sources believed to be reliable, but no guarantee is given as to their accuracy or completeness. All expressions of opinion are subject to change without notice.

Short selling involves additional investment risks and transaction costs, and creates leverage, which can increase the risk and volatility of a fund.

Asset allocation does not assure profit or protect against risk.

Nathan J. Rowader is a registered representative of ALPS Distributors, Inc.


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